基于KPCA-PSO-SVM的径流预测研究

为提高径流预测模型的准确性与稳定性,对KPCA-PSO-SVM的径流预测方法进行了研究。在分析径流影响因素的基础上,利用核主成分分析(KPCA)法对径流影响因子进行非线性特征提取,获得主成分作为支持向量机(SVM)的输入变量,建立了径流预测SVM模型,其中模型参数通过粒子群算法(PSO)进行优化。模型建立后,以新疆伊犁河雅马渡站中长期径流预测为例进行分析。预测分析结果表明,在拟合和检验阶段模型的平均相对误差分别为0.77%和7.64%,与其他预测模型比较,基于KPCA-PSO-SVM方法建立的径流预测模型有较好的预测和泛化能力,是一种行之有效的中长期径流预测方法。

Research on runoff forecast based on KPCA-PSO-SVM

To increase accuracy and stability of the runoff forecast model, KPCA-PSO-SVM based runoff forecast method is researched. On the basis of analyzing the influential factors of runoff forecast, the kernel principal component analysis (KPCA) is employed to extract main features from runoff influential factors and the principal component is used as the input of support vector machine (SVM), and the runoff forecast model is built. In the model, the parameter is optimized by the particle swarm optimization (PSO) algorithm. Finally, the model is applied to forecast the runoff at the Yamadu station on Ili River and the result shows that during the training and validation period, the average relative error are 0.77% and 7.64% respectively. By comparison with other forecast models, the fitting and generalization of this model are the best, thus it can be used as an optional method in long term runoff forecast.